Hybrid Transformer Dialog Processor for Policy Compliance
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Solution Overview
Problem
Existing dialog processing systems face challenges in maintaining accuracy and efficiency due to the complexity of managing increasing rules and conditions in rule-driven systems, and the slowness of data-driven models in responding to changing policies and broadening subject matter.
Innovation Solution
A system that combines a transformer-based dialog embedding with a rule-based classifier, where the transformer is pre-trained using dialog history data and fine-tuned by a task-specific rule-based classification layer, allowing for efficient updates of rules and conditions through an interactive dialog tree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a rule-driven system is used to process queries, then the system can provide structured responses based on defined policies, but the number of rules and conditions must increase as queries broaden subject matter, making maintenance complex and reducing efficiency
Solution Approach 1:
The system segments the dialog processing into two independent components: a rule-driven policy manager that handles structured policies, and a transformer-based response generator that handles open-ended queries. This segmentation allows each component to specialize, with the transformer component automatically adapting to new subject matters without requiring proportional increases in rules.
Solution Approach 2:
The transformer-based response generator acts as an intermediary between the user query and the policy rules. Instead of directly matching queries to rules (which increases complexity), the transformer intermediates by generating responses that naturally align with policies, reducing the need for explicit rule expansion.
2Adaptability or versatility
If pre-trained data-driven generative models are used to respond to queries, then the system can handle multiple tasks and broad subject matter, but the models are slow to respond to changing policies because time-consuming re-training is required, reducing accuracy in the interim
Solution Approach 1:
The system segments the AI functionality into a pre-trained transformer base model that provides general multi-task capability, and a task-specific fine-tuning layer that adapts to changing policies. This segmentation allows the base model to remain stable while the fine-tuning layer quickly adapts to new requirements without requiring complete re-training.
Solution Approach 2:
The transformer-based model is pre-trained on diverse dialog data before being deployed for specific tasks. This preliminary action establishes a robust foundation of multi-task capability, allowing the system to quickly adapt to new policies through fine-tuning rather than requiring time-consuming re-training from scratch.
3Adaptability or versatility
If the number of rules and conditions is increased to handle broader queries, then subject matter coverage improves, but maintaining integrity and consistency becomes complex, requiring high levels of administrative oversight
Solution Approach 1:
The transformer-based response generator provides self-service capability by automatically generating responses that align with policies without requiring manual rule configuration for each scenario. The model learns from dialog history and autonomously determines appropriate responses, reducing the administrative oversight needed to maintain rule consistency.
Solution Approach 2:
The system incorporates feedback mechanisms where dialog history is continuously analyzed to improve the transformer model's performance. This feedback loop allows the system to automatically learn from past interactions and refine its response generation, reducing the need for manual rule updates and administrative oversight.
4Reliability
If rule-based systems are used for dialog processing, then policy compliance is maintained, but the system becomes inefficient as the number of rules increases, reducing productivity
Solution Approach 1:
The system merges the strengths of rule-based and data-driven approaches by combining a policy manager that ensures compliance with a transformer-based generator that provides efficient response synthesis. The transformer component processes multiple policy constraints simultaneously and generates compliant responses without the inefficiency of traditional rule matching for each constraint.
Data Source
AI summary
Systems and methods are provided for determining a response to a query in a dialog. An entity extractor extracts rules and conditions associated with the query and determines a particular task. The disclosed technology generates a transformer-based dialog embedding by pre-training a transformer using dialog corpora including a plurality of tasks. A task-specific classifier generates a first set of candidate responses based on rules and conditions associated with the task. The transformer-based dialog embedding generates a second set of candidate responses to the query. The classifier accommodates changes made to a task by an interactive dialog editor as machine teaching. A response generator generates a response based on the first and second sets of candidate responses using an optimization function. The disclosed technology leverages both a data-driven, generative model (a transformer) based on dialog corpora and a user-driven, task-specific rule-based classifier that accommodating updates in rules and conditions associated with a particular task.